Bradley C Love
Dados Biográficos
| ID | 1463656 |
|---|---|
| NOME | Bradley C Love |
| PRENOMES | Bradley C |
| SOBRENOME | Love |
| ASSINATURA | LOVE B C |
| AFILIAÇÕES | Turing Institute |
| ORCID | 0000-0002-7883-7076 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 10 |
| TOTAL DE CITAÇÕES | 1 |
| TOTAL COMO AUTOR | 10 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2004 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2024 |
| ÍNDICE H | 1 |
Large language models surpass human experts in predicting neuroscience results
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…
Robust priors for regularized regression
Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for…
When unsupervised training benefits category learning
Humans continuously categorise inputs, but only rarely receive explicit feedback as to whether or not they are correct. This implies that they may be integrating unsupervised information together with their sparse supervised data - a form of semi-supervised learning. However, experiments testing semi-supervised learning are rare, and are bedevilled with conflicting results about whether the unsupervised information affords any benefit. Here, we s…
System alignment supports cross-domain learning and zero-shot generalisation
Recent findings suggest conceptual relationships hold across modalities. For instance, if two concepts occur in similar linguistic contexts, they also likely occur in similar visual contexts. These similarity structures may provide a valuable signal for alignment when learning to map between domains, such as when learning the names of objects. To assess this possibility, we conducted a paired-associate learning experiment in which participants ma…
How decisions and the desire for coherency shape subjective preferences over time
Recent findings suggest a bidirectional relationship between preferences and choices such that what is chosen can become preferred. Yet, it is still commonly held that preferences for individual items are maintained, such as caching a separate value estimate for each experienced option. Instead, we propose that all possible choice options and preferences are represented in a shared, continuous, multidimensional space that supports generalization.…
Coherency-maximizing exploration in the supermarket
Taking More, Now
Impulsivity is a stable personality trait associated with myopic choice behavior that favors immediate rewards over larger, delayed rewards and is often characterized as maladaptive inside and outside of the laboratory. An alternative view suggests that the consequences of trait impulsivity depend on the nature of the task environment. On this view, the optimal level of impulsivity varies across task payoff structures. This hypothesis is tested i…
How Humans Teach Agents
Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition
The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other move…
Common Mechanisms in Infant and Adult Category Learning
Computational models of infant categorization often fail to elaborate the transitional mechanisms that allow infants to achieve adult performance. In this article, we apply a successful connectionist model of adult category learning to developmental data. The Supervised and Unsupervised Stratified Adaptive Incremental Network (SUSTAIN) model is able to account for the emergence of infants' sensitivity to correlated attributes (e.g., has wings and…
Large language models surpass human experts in predicting neuroscience results
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…
Common Mechanisms in Infant and Adult Category Learning
Computational models of infant categorization often fail to elaborate the transitional mechanisms that allow infants to achieve adult performance. In this article, we apply a successful connectionist model of adult category learning to developmental data. The Supervised and Unsupervised Stratified Adaptive Incremental Network (SUSTAIN) model is able to account for the emergence of infants' sensitivity to correlated attributes (e.g., has wings and…
Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition
The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other move…
Taking More, Now
Impulsivity is a stable personality trait associated with myopic choice behavior that favors immediate rewards over larger, delayed rewards and is often characterized as maladaptive inside and outside of the laboratory. An alternative view suggests that the consequences of trait impulsivity depend on the nature of the task environment. On this view, the optimal level of impulsivity varies across task payoff structures. This hypothesis is tested i…
How Humans Teach Agents
Coherency-maximizing exploration in the supermarket
How decisions and the desire for coherency shape subjective preferences over time
Recent findings suggest a bidirectional relationship between preferences and choices such that what is chosen can become preferred. Yet, it is still commonly held that preferences for individual items are maintained, such as caching a separate value estimate for each experienced option. Instead, we propose that all possible choice options and preferences are represented in a shared, continuous, multidimensional space that supports generalization.…
Robust priors for regularized regression
Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for…
When unsupervised training benefits category learning
Humans continuously categorise inputs, but only rarely receive explicit feedback as to whether or not they are correct. This implies that they may be integrating unsupervised information together with their sparse supervised data - a form of semi-supervised learning. However, experiments testing semi-supervised learning are rare, and are bedevilled with conflicting results about whether the unsupervised information affords any benefit. Here, we s…
System alignment supports cross-domain learning and zero-shot generalisation
Recent findings suggest conceptual relationships hold across modalities. For instance, if two concepts occur in similar linguistic contexts, they also likely occur in similar visual contexts. These similarity structures may provide a valuable signal for alignment when learning to map between domains, such as when learning the names of objects. To assess this possibility, we conducted a paired-associate learning experiment in which participants ma…
Large language models surpass human experts in predicting neuroscience results
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…
Computer Science (8 obras) · Psychology (8 obras) · Artificial Intelligence (5 obras) · Cognitive psychology (5 obras) · Artificial Intelligence (4 obras) · Decision-Making and Behavioral Economics (4 obras) · Machine learning (4 obras) · Categorization (3 obras) · Child and Animal Learning Development (3 obras) · Cognitive science (3 obras)